# Private measurement of nonlinear correlations between data hosted across multiple parties

arXiv: https://arxiv.org/abs/2110.09670 · HTML: https://mlsanity.com/paper/2110.09670/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 19 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing](https://mlsanity.com/paper/1602.03090/) (2016)
- [Fast Computing for Distance Covariance](https://mlsanity.com/paper/1410.1503/) (2014)
- [Private Causal Inference](https://mlsanity.com/paper/1512.05469/) (2015)
- [Improved Differentially Private Analysis of Variance](https://mlsanity.com/paper/1903.00534/) (2019)
- [A fast algorithm for computing distance correlation](https://mlsanity.com/paper/1810.11332/) (2018)
- [The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing](https://mlsanity.com/paper/1806.05514/) (2018)
- [A Statistically and Numerically Efficient Independence Test based on Random Projections and Distance Covariance](https://mlsanity.com/paper/1701.06054/) (2017)
- [Universally Consistent K-Sample Tests via Dependence Measures](https://mlsanity.com/paper/1910.08883/) (2019)

## Built on by (most cited first, 1 of 1)

- [Strategic Coalition for Data Pricing in IoT Data Markets](https://mlsanity.com/paper/2206.07785/) (2022)

## Abstract (arXiv)

We introduce a differentially private method to measure nonlinear correlations between sensitive data hosted across two entities. We provide utility guarantees of our private estimator. Ours is the first such private estimator of nonlinear correlations, to the best of our knowledge within a multi-party setup. The important measure of nonlinear correlation we consider is distance correlation. This work has direct applications to private feature screening, private independence testing, private k-sample tests, private multi-party causal inference and private data synthesis in addition to exploratory data analysis. Code access: A link to publicly access the code is provided in the supplementary file.